In-Home Monitoring Sleep Turnover Activities and Breath Rate via WiFi Signals
Bibliographic record
Abstract
In-home sleep monitoring is essential for evaluating sleep quality of individuals. Although many sleep monitoring systems have been developed recently, they have limitations in achieving a good performance at low cost. To address this issue, this article proposes a new system based on channel-state information of domestic WiFi network to monitor both turnover activities and breathing rate of sleepers. Unlike recent approaches placing receiving antennas close to each other, scattered placement is adopted to fully exploit spatial diversity of receiving antennas. More importantly, a new error correction method is proposed to accurately recognize turnover activities. Based on the interrelation between consecutive activities, the proposed method can effectively correct the recognition errors of existing methods including convolutional neural network. Then, for accurately estimating breathing rate, both a new subcarrier selection method and a new peak identification method are proposed. Experiment results show that our system can significantly improve the recognition accuracy of eight typical sleep turnover activities and four typical sleep postures. We can achieve the mean accuracy of 94.59% and 95.83% on the recognition of turnover activities and sleep postures, respectively. Besides, our system can also significantly improve the estimation accuracy of breathing rate especially in tough scenarios, such as prone and side-lying positions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".